Triple
T32843164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | campus central services of the University of Warwick |
E840026
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
e-learning and educational technology services of the University of Warwick
The e-learning and educational technology services of the University of Warwick provide institution-wide support, platforms, and expertise for technology-enhanced teaching, learning, and assessment.
|
E840026
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: e-learning and educational technology services of the University of Warwick | Statement: [campus central services of the University of Warwick, hasComponent, e-learning and educational technology services of the University of Warwick]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: e-learning and educational technology services of the University of Warwick Triple: [campus central services of the University of Warwick, hasComponent, e-learning and educational technology services of the University of Warwick]
Generated description
The e-learning and educational technology services of the University of Warwick provide institution-wide support, platforms, and expertise for technology-enhanced teaching, learning, and assessment.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f3493ff0888190b51e974eae2a7834 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6ce366e6c8190918068e6b841a522 |
completed | May 3, 2026, 4:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a34bcfe0100819093b24a64350f36a3 |
completed | June 19, 2026, 3:52 a.m. |
| NEDg | Description generation | batch_6a34bda4d1308190932b182fc3daee1f |
completed | June 19, 2026, 3:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a34be49d2c0819089cb85ac34fa49ca |
completed | June 19, 2026, 3:58 a.m. |
Created at: May 1, 2026, 1:16 a.m.